title: [Application of radial basis function neural network for grading of gliomas].
abstract: This retrospective investigation was directed to the applicability of Radial Basis Function Neural Network (RBF-NN) and Discriminant Analysis in the grading of gliomas. The data on 116 patients with primary glioma in our hospital from February 2008 to April 2009 were collected. Kruskal-Wallis H test was used to draw in the variable age ranks and then to take them out from the range of different grades of gliomas. The results of RBF-NN model, discriminant analysis, and the combined model of RBF-NN and discriminant analysis were evaluated and compared respectively with and without age. In this study, different classifications of gliomas showed statistically significant differences in age: and the accuracy of the models with age was better than the ones without age. The predictive accuracy and Kappa value of RBF-NN model and the combined model were also better than those exhibited by Bayes discriminant analysis. Consequently, as a prediction model, or to help other models, RBF-NN is of significance to predicting the grade of gliomas.
authors.affiliation: Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, China.
authors.forename: Zhongxin
authors.initials: Z
authors.lastname: Zhao
authors.forename: Kai
authors.initials: K
authors.lastname: Lan
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.forename: Jiahe
authors.initials: J
authors.lastname: Xiao
authors.forename: Min
authors.initials: M
authors.lastname: He
mesh_terms: D001932:Brain Neoplasms / Q000473:pathology*; D005910:Glioma / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D060787:Neoplasm Grading; D016571:Neural Networks, Computer